Search Results for author:
Found 6 papers, 0 papers with code
MonoHair: High-Fidelity Hair Modeling from a Monocular Video
For the interior, we employ a data-driven, multi-view 3D hair reconstruction method.
Learning a Generalized Physical Face Model From Data
Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits.
An Implicit Physical Face Model Driven by Expression and Style
At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities.
Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation.
Efficient Incremental Potential Contact for Actuated Face Simulation
We present a quasi-static finite element simulator for human face animation.
NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations
To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features.
